Interactions between dupilumab and vitiligo: A scoping review
Bibliographic record
Abstract
In recent years, dupilumab has gained widespread recognition for its efficacy in patients with moderate to severe atopic dermatitis recalcitrant to other topical and systemic therapies.1 Controlled trials and cohort studies have highlighted dupilumab's favourable safety profiles. However, a range of adverse events, including vitiligo (OR = 2.42), have been observed.2 The existing literature offers limited guidance on managing the varied impacts of dupilumab on vitiligo, encompassing both potential improvement and exacerbation. Therefore, our study seeks to examine the current literature, aiming to formulate comprehensive management strategies for the effects of dupilumab on vitiligo. We searched MEDLINE and Embase on 1 March 2024, using keywords related to AD and vitiligo per PRISMA-ScR guidelines.3 To be eligible for inclusion, results had to be peer-reviewed manuscripts available in the English language. A total of eight studies encompassing 21 patients were ultimately included in the review, comprising five case reports, one case series and two retrospective chart reviews. Study quality was assessed using the Joanna Briggs Institute critical appraisal tool. The patient cohort was 50% female (n = 6, 12/21) with an average age of 47.5 years old (12/21). The average length of atopic dermatitis before commencing dupilumab was 11.8 years (3/21). Treatments prior to dupilumab included systemic therapies (n = 5, 26.3%) such as corticosteroids, antihistamines, biologics and immunosuppressive agents, as well as topical corticosteroids (n = 5, 26.3%) and phototherapy (n = 4, 21.1%). In total, 90.5% of patients (19/21) reported occurrence or exacerbation of vitiligo, and 9.5% (2/21) reported improvement of vitiligo while on dupilumab. Vitiligo lesion resolution was reported in 75% of patients (n = 9, 12/21), with the most common vitiligo treatments utilized in conjunction with dupilumab including corticosteroids and immunosuppressive agents (91.7%, 12/21) or phototherapy (91.7%, 12/21). In total, 33.3% of patients had a history of vitiligo (n = 4, 12/21) prior to starting dupilumab, and dupilumab cessation occurred in 40% of patients (n = 4, 10/21). In patients who developed vitiligo, the average duration until vitiligo presentation after starting dupilumab was 3.11 months (10/21) with a range from 1 to 6 months. Patient characteristics are reported in Tables 1 and 2. The occurrence or resolution of vitiligo while taking dupilumab may occur due to an imbalance in T helper pathways resulting in immunomodulation and subsequent T regulatory responses.4, 5 This imbalance may indirectly affect Th17 cell activity, potentially influencing the immune environment in a way that favours either resolution or exacerbation of vitiligo. While this immunomodulation reduces atopic dermatitis severity, it is difficult to predict the impact on lesions in patients who already have vitiligo. Although the risk is minimal, physicians should make patients aware of the possibility of vitiligo development, particularly counselling patients to closely monitor skin pigmentation changes in the first 6 months on dupilumab, as this is the time frame in which vitiligo has been reported to present. Additionally, lesion re-pigmentation on dupilumab was uncommon in our cohort of patients and may not be expected in patients receiving dupilumab for atopic dermatitis who have vitiligo based on our findings. Future research is warranted to elucidate the mechanism by which dupilumab causes re-pigmentation and assess its potential therapeutic role in the treatment of vitiligo with the goal of optimizing management and patient outcomes. Limitations of this study include a small sample size and unclear risk of bias. None. Dr. Vincent Piguet has received honoraria or fees for consulting and/or speaking for AbbVie, Almirall, Celgene, Janssen, Novartis and Pfizer and has received departmental support for Cardiff University from AbbVie, Almirall, Alliance, Beiersdorf UK Ltd, Biotest, Celgene, Dermal, Eli Lilly, Galderma, Genus Pharma, Globe Micro, Janssen-Celag, La Roche-Posay, L'Oreal, LEO Pharma, Meda, MSD, Novartis, Pfizer, Sinclair Pharma, Spirit, Stiefel, Samumed, Thornton Ross, TyPham and UCB and for University of Toronto from Sanofi. Shanti Mehta and Dea Metko have no conflicts of interest to declare. There is no ethical approval applicable. The data that support the findings of this study are available from the corresponding author upon reasonable request.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".